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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
논문
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HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Towards Robust Drone Vision in the Wild
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture
Learning Degradation Representations for Image Deblurring
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning
Robust Real-World Image Super-Resolution against Adversarial Attacks
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
Criteria Comparative Learning for Real-scene Image Super-Resolution
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging
Reference-based Image Super-Resolution with Deformable Attention Transformer
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
Improved Super Resolution of MR Images Using CNNs and Vision Transformers
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
Semantic uncertainty intervals for disentangled latent spaces
Image Super-Resolution with Deep Dictionary
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
Towards Lightweight Super-Resolution with Dual Regression Learning
Single MR Image Super-Resolution using Generative Adversarial Network
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity
Rethinking Super-Resolution as Text-Guided Details Generation
Perception-Oriented Stereo Image Super-Resolution
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information Flow
Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution
Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework
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